The proposed deep neural network for gl:DSM refinement is an extension of the network presented by Bittner  [1]} based on an image-to-image translation gl:cGAN introduced by Isola  [2]}. The network jointly learns a generator and a discriminator to do the domain transfer, from a source domain, the photogrammetric gl:DSM, to a target domain, the refined gl:DSM. With the discriminator following the PatchGAN architecture proposed by Isola  [2]}, the generator has a UNet-like structure with both long skip connections from the encoders to the decoder and short skip connections in-between the residual blocks inside the encoders. To enhance the feature of building objects, we add a gl:CBAM as presented by Woo  [4]} before the decoder. The gl:CBAM is a combination of 1D channel attention and 2D spatial attention, which are sequentially multiplied to the input feature maps. The overall generator architecture is shown in fig:generator.
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